A novel neural network design-the adaptive resonance theory least mean square (ART-LMS) neural network-is proposed for the restoration of images corrupted by impulse noise. The network design is based on the concept of counterpropagation network (CPN). There is a vigilance parameter the ART network uses to automatically generate the cluster layer node for the Kohonen learning algorithm in CPN. In addition, the LMS learning algorithm is used to adjust the weight vectors between the cluster layer and the output layer for the Grossberg learning algorithm in CPN. The advantages of the ART-LMS network include an effective solution to the initial weight problem and a good ability to handle the cluster layer nodes for the CPN learning process. Experimental results have demonstrated that the proposed filter based on ART-LMS outperforms many well-accepted conventional as well as new filters in terms of noise suppression and detail preservation.
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